Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Symbolic Cluster Analysis for Distribution Valued Dissimilarity

  • Matsui, Yusuke (Graduate School of Information Science and Technology, Hokkaido University) ;
  • Minami, Hiroyuki (Information Initiative Center, Hokkaido University) ;
  • Misuta, Masahiro (Information Initiative Center, Hokkaido University)
  • Received : 2014.01.09
  • Accepted : 2014.05.07
  • Published : 2014.05.31
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Abstract

We propose a novel hierarchical clustering for distribution valued dissimilarities. Analysis of large and complex data has attracted significant interest. Symbolic Data Analysis (SDA) was proposed by Diday in 1980's, which provides a new framework for statistical analysis. In SDA, we analyze an object with internal variation, including an interval, a histogram and a distribution, called a symbolic object. In the study, we focus on a cluster analysis for distribution valued dissimilarities, one of the symbolic objects. A hierarchical clustering has two steps in general: find out step and update step. In the find out step, we find the nearest pair of clusters. We extend it for distribution valued dissimilarities, introducing a measure on their order relations. In the update step, dissimilarities between clusters are redefined by mixture of distributions with a mixing ratio. We show an actual example of the proposed method and a simulation study.

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References

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